A Decentralized Approach to Video Copyright Protection Using Private Blockchain and Perceptual Hashing

Summary

This study presents a decentralized framework for video copyright protection that integrates private blockchain, smart contracts, and perceptual hashing techniques. The aim is to enable secure ownership registration, immutable record management, and efficient similarity-based verification of video content in distributed environments.

Abstract

The growing popularity of video content sharing on the Internet has raised concerns about copyright violations, piracy, and ownership disputes. Traditional centralized methods for handling copyright face transparency issues, susceptibility to manipulation, and reliance on trusted third parties. Therefore, this study presents a decentralized video copyright protection system based on Hyperledger Fabric, smart contracts, and perceptual hashing. Firstly, the frames from the videos are extracted, and perceptual fingerprints are created using the average hash (aHash), difference hash (dHash), perceptual hash (pHash), and wavelet hash (wHash) algorithms. Hashes and ownership information are stored on the blockchain, while multimedia assets are kept outside the ledger. Copyright checks are performed using frame-level similarity comparisons based on the Hamming distance. The proposed framework was tested across various temporal, spatial, and compressive manipulations, including frame insertion, deletion, cropping, resizing, rotation, filtering, and transcoding. The experiments showed high robustness to such changes while simultaneously detecting unauthorized modifications. Therefore, this technology can provide a practical solution to copyright issues using a decentralized blockchain framework.

Introduction

Advancements in digital multimedia technologies have changed the way videos are produced, distributed, and consumed. Video streaming, social networks, and Internet-based content-sharing systems enable the quick and easy distribution of videos to audiences worldwide. While technological advancements allow more creative possibilities in the production of digital video content and the growth of online business, these same advancements have made it easier for content creators to experience copyright violations, piracy, and problems with ownership¹. Videos can easily be copied, edited, re-encoded, and redistributed, making protecting intellectual pr....

Protocol

This study did not involve human participants, human subjects, animal subjects, or clinical specimens. Therefore, institutional ethics approval and informed consent were not required.

Materials and software 

The environment used to deploy the framework was Hyperledger Fabric version 2.4.8, CouchDB, Docker Containers, Minifab deployment, FFmpeg multimedia processing applications, and the Go programming language. Hyperledger Fabric provided services for decentralized ledger management and smart contract execution, while CouchDB stored and retrieved copyright information efficiently via off-chain....

Results

To assess the efficacy of the proposed decentralized copyright protection model, it is tested against various video manipulation attacks that frequently occur during multimedia transmission, editing, and copyright infringement. Two major factors in the assessment include: (i) the reliability of the perceptual hashing algorithm in detecting copyright and tampering, and (ii) the performance of the Hyperledger Fabric blockchain network. Similarity scores were generated by calculating average hash (aHash), difference hash (d.......

Discussion

As the proposed framework demonstrates, integrating blockchain with perceptual hashing can effectively protect videos from copyright infringement in a decentralized manner. First, by combining immutable ownership registration and perceptual hashing, it satisfies both the primary conditions of copyright management systems: secure ownership registration and identification of copyrighted materials. Second, using Hyperledger Fabric enables access control, transparent transaction handling, and tamper-proof record management.<.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CouchDB	Apache Software Foundation	https://couchdb.apache.org	NoSQL database used for off-chain storage of video metadata and multimedia content.
Docker	Docker Inc.	https://www.docker.com	Containerization platform used to deploy blockchain nodes, CouchDB instances, and supporting services.
FFmpeg	FFmpeg Developers	https://ffmpeg.org	Multimedia processing framework used for video decoding, frame extraction, and preprocessing.
Go Programming Language	Google LLC	Version 1.20+; https://go.dev	Programming language used to develop the vitChain smart contract (chaincode).
Google Drive	Google LLC	https://drive.google.com	Storage platform used for hosting and retrieving experimental video datasets.
Hyperledger Fabric	Hyperledger Foundation	Version 2.4.8; https://hyperledger-fabric.readthedocs.io	Permissioned blockchain platform used for decentralized copyright registration, transaction validation, and ledger management.
ImageHash Library	Loupax	https://github.com/loupax/imagehash	Library used to generate perceptual hash values for video-frame authentication.
Minifab	IBM / Hyperledger Community	https://github.com/hyperledger-labs/minifab	Lightweight deployment and orchestration tool used to configure and manage the Hyperledger Fabric network.
Ubuntu Linux	Canonical Ltd.	Ubuntu 22.04 LTS	Operating system used for blockchain deployment and experimental evaluation.
Visual Studio Code	Microsoft Corporation	https://code.visualstudio.com	Integrated development environment used for source code development and debugging.